Jing Lian

1.2k total citations
62 papers, 721 citations indexed

About

Jing Lian is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Neurology. According to data from OpenAlex, Jing Lian has authored 62 papers receiving a total of 721 indexed citations (citations by other indexed papers that have themselves been cited), including 34 papers in Computer Vision and Pattern Recognition, 18 papers in Media Technology and 11 papers in Neurology. Recurrent topics in Jing Lian's work include Advanced Image Fusion Techniques (14 papers), Brain Tumor Detection and Classification (11 papers) and Image and Signal Denoising Methods (7 papers). Jing Lian is often cited by papers focused on Advanced Image Fusion Techniques (14 papers), Brain Tumor Detection and Classification (11 papers) and Image and Signal Denoising Methods (7 papers). Jing Lian collaborates with scholars based in China, United Kingdom and United States. Jing Lian's co-authors include Yide Ma, Jizhao Liu, Xinguo Zhang, Yunliang Qi, Shouliang Li, Zhen Yang, Zhen Yang, Wenhao Sun, Meng Lou and Qidong Liu and has published in prestigious journals such as PLoS ONE, Advanced Functional Materials and Food Chemistry.

In The Last Decade

Jing Lian

51 papers receiving 698 citations

Peers

Jing Lian
Comparison fields: 5 of 95
  • Computer Vision and Pattern Recognition 398
  • Artificial Intelligence 167
  • Media Technology 152
  • Radiology, Nuclear Medicine and Imaging 91
  • Electrical and Electronic Engineering 86
Replace Ghada M. El‐Banby with:
Ghada M. El‐Banby Egypt
Yizeng Han China
Mohamed A. Mohamed Egypt
Vikas Singh India
Kun Yuan China
Sheng Li China
S. Srinivas Kumar India
Jzau‐Sheng Lin Taiwan
Hossein Khosravi Iran
Ghada M. El‐Banby Egypt View profile →
Citations per field, relative to Jing Lian
Jing Lian · 1×
Citations per year, relative to Jing Lian
Jing Lian · 1×

Countries citing papers authored by Jing Lian

Since Specialization
Citations

This map shows the geographic impact of Jing Lian's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jing Lian with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jing Lian more than expected).

Fields of papers citing papers by Jing Lian

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jing Lian. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jing Lian. The network helps show where Jing Lian may publish in the future.

Co-authorship network of co-authors of Jing Lian

This figure shows the co-authorship network connecting the top 25 collaborators of Jing Lian. A scholar is included among the top collaborators of Jing Lian based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Jing Lian. Jing Lian is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
# Work Indexed citations
1 8
2 1
3 0
4 0
5 0
6 0
7 0
8 0
9 0
10 1
11 9
12 4
13 4
14 7
15 35
16 12
17 1
18 2
19 14
20
Adaptive Thresholds Multi-scale Edge Detection Based on B-Spline Wavelet
1

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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